We’re very proud of our partner Matteo Della Porta and the Humanitas Research Hospital team for putting the spotlight on the use of AI in healthcare for the advancement of personalized medicine in hematology at ASH 2023 in San Diego!
- Our colleague Saverio D’Amico highlighted the advantages of adopting Generative AI and synthetic data as control arm in clinical trials.
- Gianluca Asti showed advanced methodologies for medical text report analysis to improve personalized medicine in hematology.
- Elisabetta Sauta showed innovative approaches for multimodal data integration (clinical, genomics, imaging) to improve the prediction of clinical outcome in hematological malignancies.
- Luca Lanino provided an oral presentation on data-driven harmonization for current MDS classifications.
Scroll down for the full list of research topics covered during their presentations at this global event hosted by the American Society of Hematology.
Synthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. We aimed to (1) apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; (2) develop a synthetic validation framework to assess data fidelity and privacy preservability; and (3) test the capability of synthetic data to accelerate clinical/translational research in hematology.
The availability of multimodal patient data, such as demographics, clinical, imaging, treatment, quality of life, outcomes and wearables data, as well as genome sequencing, have paved the way for the development of multimodal clinical solutions that introduce personalized or precision medicine. The clinical report is an information layer that contains relevant information about the disease in addition to the patient’s point of view. Natural language processing (NLP) is a branch of artificial intelligence (AI) and its pre-trained language models are the key technology for extracting value from this data layer.
Hematological malignancies are rare and complex diseases and as a consequence, multimodal data (ranging from clinical and genomic information to images) are required to improve diagnosis, prognosis and personalized treatments. However, collecting all these layers of information is challenging, in particular when collecting cytological and histological images from the bone marrow (BM) reproducing disease morphologic features. Synthetic data generation by Artificial Intelligence (AI) can circumvent these issues by generating images conditioned from textual inputs (i.e. reports from pathologists), which are widely available and contain many useful clinical information. This technology can enrich data with synthetic images, thus boosting translational research and improving the performances of precision medicine strategies based on multimodal information.
Myelodysplastic syndromes (MDS) are myeloid neoplasms characterized by peripheral blood cytopenias and risk of progression to acute myeloid leukemia (AML). Disease management is challenged by heterogeneity in clinical courses and survival probability. Recently, the genomic screening integration (by Molecular International Prognostic Scoring System, IPSS-M) into patient’s assessment has resulted into a significant improvement in predicting clinical outcomes compared to the conventional prognostic score (Revised IPSS, IPSS-R). Many of the consequences of genetic and cytogenetic alterations will affect gene expression by means of transcriptional and epigenetic instability and altered microenviromental signaling. The aim of this project conducted by GenoMed4All and Synthema EU consortia is to link genomic information with transcriptomic data for possibly improving the prediction of clinical outcomes in MDS patients.
The inclusion of gene mutations and chromosomal abnormalities in the 2022 WHO and ICC Classifications of MDS has enhanced diagnostic precision and is expected to improve clinical decision-making process. Although these two systems share similarities, clinically relevant discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. In this study on behalf of the International Consortium for MDS (icMDS), we adopted a data-driven approach to provide a harmonization roadmap between the 2022 WHO and ICC classification for MDS. A modified Delphi Process consensus approach is currently ongoing among icMDS experts to finalize a harmonized MDS classification scheme.